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- E. Lamma, M. Manservigi, P. Mello, F. Riguzzi, R. Serra, S. Storari,
A System for Monitoring Nosocomial Infections, IDAMAP2000, Berlin, Germany,
Abstract: In this work, we describe a project, jointly started by
DEIS University of Bologna and Dianoema S.p.A., in order to build a system
which is able to monitor nosocomial infections. To this purpose, the
system computes various statistics that are based on the count of patient
infections over a period of time. The precise count of patient infections
needs a precise definition of bacterial strains. In order to find
bacterial strains, clustering has been applied on the microbiological
data collected along two years in an Italian hospital.
- E. Lamma, M. Manservigi, P. Mello, F. Riguzzi, R. Serra, S. Storari,
A System for Monitoring Nosocomial Infections, ISMDA2000, Frankfurt, Germany,
Lecture Notes in Computer Science, vol. 1933,
Abstract: In this work, we describe a project, jointly started by
DEIS University of Bologna and Dianoema S.p.A., in order to build a system
which is able to monitor nosocomial infections. To this purpose, the system
computes various statistics that are based on the count of patient infections
over a period of time. The precise count of patient infections needs a
precise definition of bacterial strains that is found by applying clustering
to data on past infections. Moreover, the system is able to identify
critical situations for a single patient (e.g., unexpected antibiotic
resistance of a bacterium) or for hospital units (e.g., contagion events)
and alarm the microbiologist.
- E. Lamma, M. Manservigi, P. Mello, A. Nanetti, F. Riguzzi, S. Storari,
The Automatic Discovery of Alarm Rules for the Validation of Microbiological
Data, IDAMAP2001, London, UK,
Abstract: In this work, we describe a project, jointly started
by University of Bologna and Dianoema S.p.A. in order to build a system
which is able to validate microbiological data. Within the project we have
experimented data mining techniques in order to automatically discover
association rules from microbiological data, and obtain from them alarm
rules to be used for data validation. To this purpose, we have exploited
the WEKA system and applied it to a database containing data about
bacterial antibiograms. Discovered association rules are then transformed
into alarm rules, to be used for data validation within an expert system
named ESMIS. Among automatically produced alarm rules, we have identified
some already considered in ESMIS and suggested by experts according to the
NCCLS compendium, and new rules which were not present in that report, but
were recommended by interviewed microbiologists.
- E.Lamma, P.Mello, A.Nanetti, F.Riguzzi, S.Storari, Discovering
Validation Rules from Microbiological Data, New Generation Computing
special issue on Chance Discovery edited by Yukio Ohsawa and Akinori
Abe, planned for Vol.21, No.1, November 2002,
Abstract: A huge amount of data is daily collected from clinical mi-crobiology
laboratories. These data concern the resistance or susceptibil-ity
of bacteria to tested antibiotics. Almost all microbiology laboratories
follow standard antibiotic testing guidelines which suggest antibiotic test
execution methods and result interpretation and validation (among them,
those annually published by NCCLS). Guidelines basically specify, for
each species, the antibiotics to be tested, how to interpret the results of
tests and a list of exceptions regarding particular antibiotic test results.
Even if these standards are quite assessed, they do not consider pecu-liar
features of a given hospital laboratory, which possibly influence the
antimicrobial test results, and the further validation process.
In order to improve and better tailor the validation process, we have
applied knowledge discovery techniques, and data mining in particular,
to microbiological data with the purpose of discovering new validation
rules, not yet included in NCCLS guidelines, but considered plausible and
correct by interviewed experts. In particular, we applied the knowledge
discovery process in order to find (association) rules relating to each other
the susceptibility or resistance of a bacterium to different antibiotics.
This approach is not antithetic, but complementary to that based on
NCCLS rules: it proved very effective in validating some of them, and
also in extending that compendium. In this respect, the new discovered
knowledge has lead microbiologists to be aware of new correlations among
some antimicrobial test results, which were previously unnoticed. Last
but not least, the new discovered rules, taking into account the history
of the considered laboratory, are better tailored to the hospital situation,
and this is very important since some resistances to antibiotics are specific
to particular, local hospital environments.
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